Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs-Reference-Cited by-同舟云学术

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Sharpshooting Most Beneficial Part of AUC for Detecting Malicious Logs

Published:2021 Issue: Volume: Page:31-46
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Nishiyama Taishi,Kumagai Atsutoshi,Fujino Akinori,Kamiya Kazunori

Publisher

Springer Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-16-8531-6_3

Reference31 articles.

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2. Jang, J., Brumley, D., Venkataraman, S.: BitShred: feature hashing malware for scalable triage and semantic analysis. In: CCS, pp. 309–320. ACM (2011)

3. Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. In: NDSS. Internet Society (2019)

4. Copty, F., Danos, M., Edelstein, O., Eisner, C., Murik, D., Zeltser, B.: Accurate malware detection by extreme abstraction. In: ACSAC, pp. 101–111. ACM (2018)

5. Lecture Notes in Computer Science;C Wressnegger,2016

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Malicious Log Detection Using Machine Learning to Maximize the Partial AUC;2024 IEEE 21st Consumer Communications & Networking Conference (CCNC);2024-01-06

2. Transfer Anomaly Detection for Maximizing the Partial AUC;2022 International Joint Conference on Neural Networks (IJCNN);2022-07-18

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